Dissecting the digital divide: A household fixed effects approach to estimating gender gaps in digital skills of youth in low- and middle-income economies.

This study investigates gender gaps in digital skills among youth (15-24 years old) in 32 low- and middle-income economies using data from UNICEF-supported and internationally comparable Multiple Indicator Cluster Surveys (MICS). Utilizing a household fixed effects approach, we aim to isolate gender-based disparities from household-level variations. The intra-household analysis reveals significant inequalities, with biases against young women in possessing digital skills, including the very basic ones. Supplementary analysis using a mixed-effects model, which accounts separately for within- and between-household variation, highlights that wealthier households exhibit larger gender gaps in digital skills, disadvantaging young women primarily due to a floor effect in the poorest households. The paper concludes with policy implications aimed at reducing gender gaps in digital skills.

Bullying in the Russian Secondary School: Predictive Analysis of Victimization.

Background: Bullying has been recognized as an important risk factor for personal development in adolescence. Although numerous studies report high prevalence of bullying in Russian schools, limited research was based on the large-scale, nationally representative analysis, which highlights the lack of findings applicable to the national context. Objective: This study aims to address the following research questions: (1) What is the bullying victimization prevalence in Russian secondary schools? (2) What is the socio-demographic profile of the bullying victims? (3) To what extent do learning outcomes in core subject domains predict bullying? (4) How does psychological climate at school affect the occurrence of bullying? (5) Which emotional states do bullying victims typically display? (6) Which psychosocial traits are the most common for bullying victims? Data and Methods: The study adopts the statistical analysis of the Programme for International Student Assessment (PISA) data in Russia. The final sample consists of 6,249 children aged 15 years who answered the bullying questions. K-means clustering approach was adopted to identify schoolchildren who should be classified as bullying victims amongst those who have reported bullying. Logistic regression was used to estimate the probability change of bullying under different psychosocial factors and examine the effect of bullying on the emotional states of the victims. Results: The results of the study reveal that 16% of children are victims of bullying in the Russian secondary school. Bullying is strongly associated with learning outcomes in reading, thus outlining that low performers are at risk of severe victimization. Bullying is also contingent on the psychological climate and tends to develop more frequently in a competitive environment. The findings outline that bullying increases negative feelings such as misery, sadness, and life dissatisfaction amongst its victims, making a substantial footprint on their lives. Logically, bullying victims are less likely to feel happy and joyful. Finally, it was revealed that bullying victims do not tend to share negative attitudes to the per se, which identifies directions for future research in this domain. Implications: Instead of dealing with the consequences of bullying, prevention strategies should aim at facilitating a positive environment at school, thus addressing the problem.

How many students could continue learning during COVID-19-caused school closures? Introducing a new reachability indicator for measuring equity of remote learning.

This paper proposes a new reachability indicator to analyze the effectiveness of remote learning policies adopted by ministries of education in response to school closures caused by the COVID-19 pandemic. The indicator provides the share of students that can potentially be reached by remote learning policies given the availability of necessary household assets such as radios, televisions, computers and internet access. The results of this analysis outline the stark inequities in access to remote learning, suggesting that at a minimum, more than 30 % of schoolchildren globally cannot be reached by remote learning policies due to the high variation in access to assets for remote learning that exists within and between the world regions. The analysis finds that wealth and area of residence are key factors affecting the reachability of remote learning policies, suggesting that children who reside in rural areas and/or belong to the poorest households in their country are at the greatest risk of being left behind.